ArticleVeterinary research2015
What variables are important in predicting bovine viral diarrhea virus? A random forest approach.
Article in Veterinary research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- XGBoost-based early warning framework for farm-level PED risk using meteorological and herd management variables.iScience · 2026Article
- Advanced machine learning approaches for predicting Neglected Tropical Disease co-endemicity in Kenya: A focus on soil-transmitted helminths, schistosomiasis, and lymphatic filariasis.PLoS neglected tropical diseases · 2026Article
- Host-Microbe Interactions: Prospects of Machine Learning and Deep Learning Technologies in Animal Viral Disease Management.Veterinary sciences · 2025Review
- Maternal administration of octanoate, a medium-chain fatty acid, improves feed efficiency of Japanese black calves through influencing gut bacteriome structure.Scientific reports · 2025Article
- Opportunities for machine learning to predict cross-neutralization in FMDV serotype O.PLoS computational biology · 2025Article
- Transmission pathways of Campylobacter jejuni between humans and livestock in rural Ethiopia are highly complex and interdependent.Gut pathogens · 2025Article
- From Prediction to Precision: Explainable AI-Driven Insights for Targeted Treatment in Equine Colic.Animals : an open access journal from MDPI · 2025Article
- Drug shortage in South Korea: machine learning-based prediction models and analysis of duration and causal factors.Frontiers in pharmacology · 2025Article
- Source attribution of humanFrontiers in microbiology · 2025Article
- Comparison of Machine Learning Tree-Based Algorithms to Predict Future Paratuberculosis ELISA Results Using Repeat Milk Tests.Animals : an open access journal from MDPI · 2024Article
- Assessing the feasibility of applying machine learning to diagnosing non-effusive feline infectious peritonitis.Scientific reports · 2024Article
- Literature Review on Technological Applications to Monitor and Evaluate Calves' Health and Welfare.Animals : an open access journal from MDPI · 2023Review
- Assessment of Ventral Tail Base Surface Temperature for the Early Detection of Japanese Black Calves with Fever.Animals : an open access journal from MDPI · 2023Article
- Longitudinal Study of Therapeutic Adherence in a Cystic Fibrosis Unit: Identifying Potential Factors Associated with Medication Possession Ratio.Antibiotics (Basel, Switzerland) · 2022Article
- Comparison of machine learning models for bluetongue risk prediction: a seroprevalence study on small ruminants.BMC veterinary research · 2022Article
- A random forest model for forecasting regional COVID-19 cases utilizing reproduction number estimates and demographic data.Chaos, solitons, and fractals · 2022Article
- Comparison of Different Machine Learning Classifiers for Glaucoma Diagnosis Based on Spectralis OCT.Diagnostics (Basel, Switzerland) · 2021Article
- Article
- Improving fake news classification using dependency grammar.PloS one · 2021Article
- Frequent Visits to an Outdoor Range and Lower Areas of an Aviary System Is Related to Curiosity in Commercial Free-Range Laying Hens.Animals : an open access journal from MDPI · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Bovine viral diarrhea virus (BVDV) causes one of the most economically important diseases in cattle, and the virus is found worldwide. A better understanding of the disease associated factors is a crucial step towards the definition of strategies for control and eradication. In this study we trained a random forest (RF) prediction model and performed variable importance analysis to identify factors associated with BVDV occurrence. In addition, we assessed the influence of features selection on RF performance and evaluated its predictive power relative to other popular classifiers and to logistic regression. We found that RF classification model resulted in an average error rate of 32.03% for the negative class (negative for BVDV) and 36.78% for the positive class (positive for BVDV).The RF model presented area under the ROC curve equal to 0.702. Variable importance analysis revealed that important predictors of BVDV occurrence were: a) who inseminates the animals, b) number of neighboring farms that have cattle and c) rectal palpation performed routinely. Our results suggest that the use of machine learning algorithms, especially RF, is a promising methodology for the analysis of cross-sectional studies, presenting a satisfactory predictive power and the ability to identify predictors that represent potential risk factors for BVDV investigation. We examined classical predictors and found some new and hard to control practices that may lead to the spread of this disease within and among farms, mainly regarding poor or neglected reproduction management, which should be considered for disease control and eradication.
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